Policy Research Working Paper 10916 Public Disclosure Authorized Public Disclosure Authorized | --- | --- | --- | --- | --- | --- | --- | |-----------------------------------------|-------|-------|-------|-------|-------|-------| | | | | | | | | | How Well Did Real-Time Indicators Track | | | | | | | | | | | | | | | | Household Welfare Changes in Developing | | | | | | | | Countries during the COVID-19 Crisis? | | | | | | | David Newhouse Rachel Swindle Shun Wang Joshua D. Merfeld Utz Pape Kibrom Tafere Mic ...

Public Disclosure Authorized ID4D DPI Public Disclosure Authorized Public Disclosure Authorized ELECTRONIC SIGNATURES ENABLING TRUSTED DIGITAL TRANSFORMATION Public Disclosure Authorized DIGITAL TRANSFORMATION POLICY NOTE SERIES SEPTEMBER 2024 © 2024 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: +1-202-473-1000; Internet: www.worldbank.org Some rights reserved. This work is a product of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Generative AI has the potential to significantly impact economic growth, labor markets, and global trade patterns, with varying predictions on its effects [8][9] - The paper introduces a multi-sector growth model to analyze the effects of generative AI, emphasizing the distinction between high-skill, highly digitalized services and low-skill, less digitalized services [4][10] - The risk of "premature de-professionalization" is highlighted, where generative AI may limit the creation of well-paid jobs in high-skill services, particularly in developing countries [4][17][23] Summary by Sections Introduction - Generative AI could increase global GDP by 7% over a decade according to Goldman Sachs, while more conservative estimates suggest a 0.9% to 1.1% increase [8] - The impact of generative AI on labor markets could lead to increased competition and lower wages in cognitive occupations [8][9] Structural Transformation - The report discusses the shift in economic activities across sectors, noting the decline of agriculture and manufacturing in favor of services [10][11] - It emphasizes the importance of understanding the interplay between growth and structural transformation for sustainable development [11][12] Model Construction - A multi-sector growth model is constructed to analyze the influence of AI on economic growth and structural transformation, incorporating demand-side factors and sectoral differences [12][20] - The model includes four sectors: agriculture, manufacturing, high-skill services, and low-skill services, with labor as the only production factor [12][55] AI's Impact on Growth - AI influences growth through demand, supply, and international production specialization channels [13][15] - Simulations reveal that unless AI achieves widespread adoption and drives transformative innovations, its growth benefits may be limited [18][23] Employment and Income Dynamics - The report predicts a stagnation or decline in high-skill services employment share, with a shift towards low-skill services due to AI advancements [16][17] - It warns that developing countries slow to adopt AI risk becoming commodity exporters, facing youth underemployment and declining living standards [17][23] Conclusion - The paper contributes to the literature by analyzing generative AI's effects through structural transformation and international production specialization [20][23] - It underscores the need for timely AI adoption to avoid economic stagnation and to foster high-skill service employment opportunities [23]

Policy Research Working Paper 10913 Public Disclosure Authorized Public Disclosure Authorized Fiscal Challenges in Small States Weathering Storms, Rebuilding Resilience Samuel Hill Jeetendra Khadan Development Economics Prospects Group September 2024 Public Disclosure Authorized Public Disclosure Authorized Policy Research Working Paper 10913 Abstract The COVID-19 pandemic and the global shocks that followed have worsened fiscal and debt positions in small states, intensifying their already substantial fisc ...

Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Policy Research Working Paper 10917 Statistically Matching Income and Consumption Data An Evaluation of Energy and Income Poverty in Romania Britta Rude Monica Robayo-Abril Poverty and Equity Global Practice September 2024 Public Disclosure Authorized Policy Research Working Paper 10917 Abstract To design effective policy instruments that target the energy poor in Romania, it is crucial to understand who the energy poor a ...

Investment Rating - The report does not explicitly provide an investment rating for the agricultural sector in Ukraine, but it discusses the effectiveness of the Producer Support Grant program, indicating a positive impact on small farmers [4][12]. Core Insights - The digital farmer registry in Ukraine, the State Agrarian Register (SAR), has been instrumental in targeting and evaluating the $50 million Producer Support Grant (PSG) program, which has significantly increased the area cultivated by small farmers, particularly those near conflict zones [4][12]. - The PSG program provided cash grants of approximately $86 per hectare, which were most beneficial for the smallest farmers (under 20 hectares) and those located closest to active fighting [12][32]. - The report emphasizes the importance of using administrative data and satellite imagery to assess agricultural support in conflict settings, highlighting the potential for improved targeting and effectiveness of public programs [13][20]. Summary by Sections Introduction - The report outlines the context of Ukraine's agricultural sector post-Russian invasion, noting significant displacement and damage to agricultural productivity [9][23]. - The SAR was established to facilitate access to support programs for small and medium-sized farmers, particularly through the PSG [9][29]. Context and Setting - Ukraine's agricultural sector is characterized by its fertile land and significant contribution to GDP and exports, with a historical backdrop of collectivization and privatization [25][26]. - The war has disrupted agricultural production and logistics, necessitating the establishment of the SAR to support farmers [10][27]. PSG Program and SAR Platform - The PSG program, targeting small farmers, provided unconditional cash grants to support working capital and prevent liquidation of farms due to war impacts [32][29]. - By June 2024, over 150,000 farmers had registered with the SAR, significantly exceeding previous estimates of agricultural producers in Ukraine [31][32]. Assessing PSG Impacts - The analysis indicates that the PSG program had a significant but modest impact on the area cultivated, particularly among small producers near conflict zones [46]. - The report utilizes a difference-in-differences design to evaluate the program's effectiveness, suggesting that targeting rules were largely adhered to, with limited mis-targeting [46][50].

Investment Rating - The report does not explicitly provide an investment rating for the industry. Core Insights - The infrastructure financing gap is substantial, with annual investments totaling approximately $2.7 trillion in 2020, leaving a shortfall of $0.7 trillion. This gap widens when considering various country targets to achieve net-zero emissions by 2050, as the energy and transport sectors account for around 60 percent of emissions. The private sector's role in bridging this gap becomes increasingly vital, especially given the fiscal constraints of emerging markets and developing economies (EMDEs) exacerbated by the COVID-19 pandemic and geopolitical tensions [39][63]. - Local Currency Financing (LCF) is emerging as a pivotal tool for unlocking financing for sustainable infrastructure development that adheres to the Green, Resilient, Inclusive Development (GRID) approach. Well-developed LCF markets can shield an economy from volatile foreign capital flows, reduce the burden of hard currency repayments, curb the accumulation of foreign debt, and tap into new domestic capital sources for development [40][66]. - The study aims to address gaps in research related to commercial financing challenges in LCF for sustainable infrastructure, focusing on credit/banking markets and the need for innovative solutions to bridge the infrastructure gap [41][42]. Summary by Sections Chapter 1: Background and Rationale - The study is part of a broader program supported by the Public-Private Infrastructure Advisory Facility (PPIAF) to address challenges in mobilizing LCF commercial lending into infrastructure and climate projects [53]. - The rationale behind country selection includes high savings rates and financial development as key factors enabling LCF of infrastructure [57]. Chapter 2: Scope and Objectives - The report identifies significant challenges for infrastructure and climate investments, including the reliance on hard currency financing despite most project revenues being in local currency [65][66]. - Key constraints identified by domestic financiers include underdeveloped domestic markets, limited risk mitigation instruments, and asset-liability mismatches due to the short tenors of deposits versus the long tenors required for infrastructure finance loans [65][66]. Chapter 3: LCF Analytical Framework - Robust ecosystems conducive to infrastructure and climate investments are characterized by a stable macroeconomic environment, high levels of contractual savings, deep and liquid capital markets, and adequate enabling regulatory frameworks [78]. - The key enablers for a robust LCF market include the scale of private savings, the ability of local credit markets to provide long-tenor debt, and the capacity for structuring and credit evaluation skills for sustainable infrastructure financing [82].

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report emphasizes the importance of developing a robust methodology for assessing poverty and social exclusion in Croatia, leveraging the forthcoming Central Register of the Population to fill data gaps and improve monitoring [19][23][26] Summary by Sections Introduction - The European Union has committed to addressing poverty and social exclusion, aiming to lift at least 15 million people out of risk by 2030 through various funding mechanisms [19][21] - Croatia has made progress in reducing poverty, with the share of the population living on less than US$ 6.85 a day decreasing from 8.1% in 2011 to 1.8% in 2021, yet certain demographic groups remain vulnerable [19][21] Overview of Key Concepts and Data Sources in the EU - The report outlines the official definitions of poverty and social exclusion indicators required by Eurostat, highlighting the shift from survey-based to administrative data [29][30] - It discusses the concept of at-risk-of-poverty (AROP) as a key indicator, defined as the share of the population with an equivalized disposable income below 60% of the national median [31][34] Croatia's Central Register of the Population - The report assesses the development of Croatia's Central Register of the Population, detailing its objectives, timeline, and legal framework [3][3][3] - It highlights the potential of the Population Register to improve data quality and fill gaps in poverty and social exclusion measurement [3][3][3] Register-Based Poverty Measurements in Croatia - The report identifies existing AROP measures and data sources in Croatia, discussing challenges such as incomplete tax income data and under-reporting of income [4][4][4] - Recommendations for improving poverty measurement methodologies are provided, including approaches to address under-reporting and spatial price differences [4][4][4] Register-Based Measurements of Social Exclusion in Croatia - The report examines the existing approach to measuring social exclusion in Croatia and offers recommendations for enhancing measurement methodologies [5][5][5] - It emphasizes the need for simplified AROPE rates and the development of social exclusion indicators by domain [5][5][5] Institutional Set-Up for Tracking Poverty and Social Exclusion - The report suggests potential institutional arrangements for data collection, analysis, and reporting to effectively monitor poverty and social exclusion indicators [6][6][6] - It outlines the roles of various stakeholders in the proposed institutional models [6][6][6] Monitoring System for Poverty and Social Exclusion - A proposed monitoring system for tracking poverty and social exclusion indicators at subnational levels is detailed, including the collection and processing of indicators [7][7][7] - The report concludes with next steps for implementing the recommendations and enhancing the monitoring framework [8][8][8]

Investment Rating - The report does not explicitly provide an investment rating for the industry under study Core Insights - Capital incentive policies in OECD countries, while aimed at promoting IT capital investment, may inadvertently hinder the adoption of cloud computing, big data analytics, and AI technologies [4][10][15] - The introduction of the Annual Investment Allowance (AIA) in the UK served as a quasi-natural experiment, revealing that while it increased IT capital investment by 61.7% from 2007 to 2013, it simultaneously reduced cloud adoption by 17 percentage points compared to the average cloud adoption rate of 28% during the same period [13][14] - The AIA's negative impact on technology diffusion was particularly pronounced for small and medium-sized enterprises (SMEs), which were found to be 37% less likely to adopt cloud technologies due to the capital incentive [14][15] Summary by Sections Introduction - The report discusses how capital incentives can shape production technology and the unintended consequences these policies may have on technological change [8][9] Policy Analysis - The AIA was introduced to stimulate investment in tangible capital, including IT capital, but has been shown to distort the choice between investing in IT capital and adopting cloud services [12][22] Empirical Findings - The empirical analysis indicates that the AIA led to a significant increase in tangible capital investment but a decrease in the adoption of cloud technologies, big data analytics, and AI [13][15] - The report estimates that the AIA policy reduced overall cloud use in the UK by 7-9 percentage points, effectively slowing cloud diffusion by more than one year [13][15] Technology Adoption - The findings suggest that the AIA also lowered the likelihood of using big data analytics and AI by 18% and 3%, respectively, among treated firms [15][16] - The report highlights that the demand for data analytics workers decreased by approximately 1.1% in firms affected by the AIA, indicating a direct link between capital incentives and labor demand in data-intensive roles [16][19] Conclusion - The report concludes that capital incentive policies can inadvertently affect the direction of technological adoption, leading to outcomes that contradict their intended objectives [18][20]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The report highlights the resilience of Guinea's economy, driven by a mining boom, but notes the challenges posed by weak linkages to the domestic economy and Dutch disease dynamics [19][20][38] - Agriculture is identified as a critical sector for structural transformation and climate resilience, with significant potential for inclusive growth [27][28] Summary by Sections Executive Summary - The report presents an overview of Guinea's macroeconomic position and emphasizes the importance of agriculture for sustainable growth [18] Chapter I: Macroeconomic and Poverty Developments and Outlook - GDP growth accelerated to 7.1% in 2023, driven by a 22% increase in bauxite production and a 10% increase in gold production [19][20] - The mining sector's weak integration with the domestic economy limits job creation and poverty reduction [20][41] - Fiscal management has maintained low deficits, averaging 1.4% annually from 2016 to 2023, but tax revenues remain low, averaging 12.7% of GDP [21][22] - The current account deficit averaged 10.6% from 2016 to 2023, primarily due to mining-related exports and FDI-related imports [24] - Growth is expected to slow to 4.9% in 2024 due to external shocks but is projected to accelerate to an average of 6.3% in 2025-2026 [25] Chapter II: The Importance of Agriculture for Structural Transformation and Climate Proofing Guinea's Economy - Agriculture contributes 27.8% to GDP and employs 53% of the population, but productivity remains low due to subsistence farming practices [28][31] - Climate change poses significant risks to agricultural productivity, with potential declines of up to 25% without appropriate measures [30] - The Nationally Determined Contribution (NDC) outlines a budget of US$13.8 billion needed by 2030 to achieve emissions reduction targets [32][33] - Policy options for fostering inclusive agricultural growth include improving public finance management, enhancing infrastructure, and investing in climate-resilient practices [34][36]